Annotating cell types in Kluc tumors
heinin
2024-02-23
Last updated: 2024-02-27
Checks: 5 2
Knit directory: PD1_mm/
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| absolute | relative |
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| /home/hnatri/PD1_mm/ | . |
| /home/hnatri/PD1_mm/code/utilities.R | code/utilities.R |
| /home/hnatri/PD1_mm/code/PD1_mm_themes.R | code/PD1_mm_themes.R |
| /home/hnatri/PD1_mm/code/CART_plot_functions.R | code/CART_plot_functions.R |
| /home/hnatri/PD1_mm/cluster_markers.tsv | cluster_markers.tsv |
| /home/hnatri/PD1_mm/Layer1_scImmuCC_label.csv | Layer1_scImmuCC_label.csv |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 18c1bd4 | heinin | 2024-02-27 | Cell type proportion testing |
| html | 18c1bd4 | heinin | 2024-02-27 | Cell type proportion testing |
| Rmd | 3e207b9 | heinin | 2024-02-26 | Added scImmuCC annotations |
| html | 3e207b9 | heinin | 2024-02-26 | Added scImmuCC annotations |
| Rmd | 407ac35 | heinin | 2024-02-26 | Updating the first script |
| html | 407ac35 | heinin | 2024-02-26 | Updating the first script |
| Rmd | 196db6a | heinin | 2024-02-26 | Starting the comparative analysis |
| html | 196db6a | heinin | 2024-02-26 | Starting the comparative analysis |
| Rmd | 627f4bf | heinin | 2024-02-23 | Updated scripts |
| html | 627f4bf | heinin | 2024-02-23 | Updated scripts |
| Rmd | 9e080d7 | heinin | 2024-02-23 | Added the initial annotation script |
| html | 9e080d7 | heinin | 2024-02-23 | Added the initial annotation script |
Introduction
Initial analysis on the scRNAseq data from Kluc tumors treated with PD1 and/or CAR T.
Packages and environment variables
suppressPackageStartupMessages({
#library(cli)
library(Seurat)
library(SeuratObject)
library(SeuratDisk)
library(tidyverse)
library(tibble)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(workflowr)
library(googlesheets4)
library(scImmuCC)})
setwd("/home/hnatri/PD1_mm/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)
# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/PD1_mm/code/utilities.R")
source("/home/hnatri/PD1_mm/code/PD1_mm_themes.R")
source("/home/hnatri/PD1_mm/code/CART_plot_functions.R")Importing data
seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")Adding human gene names to a separate assay
# Converting mouse gene names to human
mouse_human_genes <- read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt", sep="\t")
convert_mouse_to_human <- function(gene_list){
gene_names <- as.data.frame(matrix(nrow = length(gene_list),
ncol = 2))
colnames(gene_names) <- c("mouse", "human")
rownames(gene_names) <- gene_list
gene_names$mouse <- gene_list
for(gene in gene_list){
class_key = (mouse_human_genes %>% filter(Symbol == gene & Common.Organism.Name=="mouse, laboratory"))[['DB.Class.Key']]
if(!identical(class_key, integer(0)) ){
human_genes = (mouse_human_genes %>% filter(DB.Class.Key == class_key & Common.Organism.Name=="human"))[,"Symbol"]
if(length(human_genes)==0){
gene_names[gene, "human"] <- NA
} else if (length(human_genes)>1){
# human_genes <- paste0(human_genes, collapse = ", ")
bind_df <- data.frame("mouse" = rep(gene, times = length(human_genes)),
"human" = human_genes)
gene_names <- rbind(gene_names, bind_df)
} else {
gene_names[gene, "human"] <- human_genes
}
}
}
return(gene_names)
}
gene_names <- convert_mouse_to_human(rownames(seurat_data@assays$RNA))Warning: There was 1 warning in `filter()`.
ℹ In argument: `DB.Class.Key == class_key & Common.Organism.Name == "human"`.
Caused by warning in `DB.Class.Key == class_key`:
! longer object length is not a multiple of shorter object lengthlength(rownames(seurat_data@assays$RNA))[1] 32285dim(gene_names)[1] 36196 2# Keeping mouse genes with a single human ortholog
gene_names <- gene_names %>%
group_by(mouse) %>%
filter(!is.na(human),
n() == 1) %>%
ungroup()
assay_data <- LayerData(seurat_data, assay = "RNA", layer = "counts")
assay_data <- assay_data[which(rownames(assay_data) %in% gene_names$mouse),]
new_names <- rownames(assay_data)
new_names <- mapvalues(x = new_names,
from = gene_names$mouse,
to = gene_names$human)
rownames(assay_data) <- new_names
seurat_data[["RNA_human"]] <- CreateAssayObject(assay_data,
min.cells = 0,
min.features = 0)Warning: Non-unique features (rownames) present in the input matrix, making
uniquesaveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")Visualizing clusters
DimPlot(seurat_data,
group.by = "snn_res.0.5",
reduction = "umap",
raster = T,
cols = cluster_col,
label = T) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend() &
manuscript_theme
Cell type marker expression
DefaultAssay(seurat_data) <- "RNA"
plot_features <- c()
# Top markers for each cluster
markers <- presto::wilcoxauc(seurat_data,
group_by = "snn_res.0.5",
assay = "data",
seurat_assay = "RNA")
top_markers <- markers %>% group_by(group) %>% slice_max(order_by = auc, n = 2)
FeaturePlot(seurat_data,
features = top_markers$feature,
ncol = 5,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend() &
manuscript_theme
top_markers <- markers %>% group_by(group) %>% slice_max(order_by = auc, n = 5)
# seurat_object, plot_features, group_var, group_colors, column_title, km=5, row.order = NULL
dotplot_heatmap <- create_dotplot_heatmap(seurat_object = seurat_data,
plot_features = unique(top_markers$feature),
group_var = "snn_res.0.5",
group_colors = cluster_col,
column_title = "",
km = 5, row.order = NULL)
| Version | Author | Date |
|---|---|---|
| 3e207b9 | heinin | 2024-02-26 |
Saving top markers to a file
top_markers <- markers %>% group_by(group) %>% slice_max(order_by = auc, n = 20)
write.table(top_markers, "/home/hnatri/PD1_mm/cluster_markers.tsv",
quote = F, row.names = F, sep = "\t")Canonical mouse immune markers
# Mouse immune markers
gs4_deauth()
canonical_markers <- gs4_get("https://docs.google.com/spreadsheets/d/1ApwXjEVtpPB87al6q3ab8TKvZYJTh3iNH1cuO-A_OoU/edit?usp=sharing")
sheet_names(canonical_markers) [1] "Sample summary"
[2] "GSEA GBM"
[3] "Mm immune markers"
[4] "Cluster annotations, JAK mouse"
[5] "Sherri-Cluster annotations, JAK mouse"
[6] "Cluster markers, JAK mouse"
[7] "Sherri-Cluster markers, JAK mouse"
[8] "DL_cluster_res"
[9] "Cluster markers, final data, UPN109pre, no UPN208"
[10] "Cluster annotations, GBM+mouse"
[11] "Cluster markers, GBM+JAK1KO"
[12] "Sherri-Cluster markers, GBM+JAK1KO"
[13] "Cluster markers, final data, immune+fibroblast"
[14] "Cluster annotations, immune+fibroblast"
[15] "Cluster markers, immune+fibroblast, top 100"
[16] "Sherri - Cluster annotations, immune+fibroblast"
[17] "Sherri-Only GBM Charactarization 50 genes"
[18] "Sherri only GBM immune+fibroblast, top 100"
[19] "Cluster annotations"
[20] "Heatmap genes"
[21] "Tumor and CSF sample summary" mm_immune_markers <- read_sheet(canonical_markers, sheet = "Mm immune markers")✔ Reading from "13384 tumor scRNAseq tables".✔ Range ''Mm immune markers''.dotplot_heatmap <- create_dotplot_heatmap(seurat_object = seurat_data,
plot_features = mm_immune_markers$gene_name,
group_var = "snn_res.0.5",
group_colors = cluster_col,
column_title = "",
km = 5, row.order = NULL)Warning: The following requested variables were not found: Cd51, Nirp3, 117r
| Version | Author | Date |
|---|---|---|
| 3e207b9 | heinin | 2024-02-26 |
Using sc-ImmuCC to annotate immune cells
count_data <- LayerData(seurat_data, assay = "RNA_human", layer = "counts")
#scImmuCC_Layered(count = count_data, Non_Immune = FALSE)
# Importing results
scicc_labels <- read.csv("/home/hnatri/PD1_mm/Layer1_scImmuCC_label.csv",
row.names = "X")
length(colnames(seurat_data))
length(intersect(scicc_labels$barcodes, colnames(seurat_data)))
seurat_data$scImmuCC_celltype <- mapvalues(x = colnames(seurat_data),
from = scicc_labels$barcodes,
to = scicc_labels$cell_type)
# Plotting
DimPlot(seurat_data,
group.by = "scImmuCC_celltype",
reduction = "umap",
raster = T,
#cols = scImmuCC_celltype_col,
label = T) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend() &
manuscript_theme
saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")DimPlot(seurat_data,
split.by = "scImmuCC_celltype",
group.by = "snn_res.0.5",
ncol = 3,
reduction = "umap",
raster = T,
cols = cluster_col) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
table(seurat_data$scImmuCC_celltype,
seurat_data$snn_res.0.5)
0 1 2 3 4 5 6 7 8 9 10 11 12
Bcell 0 2 1 0 0 0 0 2 1 1 3 5 0
DC 33 31 17 3 75 3 0 10 2 71 4 796 37
ILC 0 0 0 2 0 0 0 0 0 0 0 3 0
Macrophage 2586 3867 990 115 1116 64 39 2437 1031 1492 1303 694 933
Mast 1 2 0 23 1 4 1 1 0 83 78 10 3
Monocyte 2230 703 2988 13 2151 6 10 159 820 644 495 374 480
Neutrophil 1 0 0 19 0 4 5 0 0 74 435 22 13
NK 0 0 0 46 0 731 2647 2 157 2 22 3 7
Tcell 5 10 0 3245 2 2413 36 9 519 4 28 17 12
13 14 15 16 17 18 19
Bcell 1314 4 0 272 0 0 0
DC 1 250 22 37 1 1 3
ILC 0 3 0 35 0 0 0
Macrophage 49 647 554 351 30 132 196
Mast 0 0 0 1 0 17 0
Monocyte 29 149 320 76 23 14 282
Neutrophil 1 79 0 4 0 26 0
NK 8 15 52 9 80 189 0
Tcell 30 75 230 53 665 196 5Adding annotations from the Google Sheet
gs4_deauth()
markers_annotations <- gs4_get("https://docs.google.com/spreadsheets/d/1iWYBouwQlQboI-rwiujC0QKJ6lq9XeTffbKm2Nz8es0/edit?usp=sharin#g")
sheet_names(markers_annotations)[1] "Cluster top markers" "Cluster annotations" "scImmuCC"
[4] "Mm immune markers" annotations <- read_sheet(markers_annotations, sheet = "Cluster annotations")✔ Reading from "PD1 mm scRNAseq tables".✔ Range ''Cluster annotations''.seurat_data$celltype <- mapvalues(seurat_data$snn_res.0.5,
from = annotations$snn_res.0.5,
to = annotations$annotation)
DimPlot(seurat_data,
group.by = "celltype",
reduction = "umap",
raster = T,
label = T) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
| Version | Author | Date |
|---|---|---|
| 18c1bd4 | heinin | 2024-02-27 |
FeaturePlot(seurat_data,
features = c("percent.mt_RNA", "nCount_RNA", "nFeature_RNA"),
reduction = "umap",
raster = T,
ncol = 3) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend()
saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.18.0 viridis_0.6.3 viridisLite_0.4.2
[4] circlize_0.4.15 plyr_1.8.8 RColorBrewer_1.1-3
[7] scImmuCC_1.0.0 GSVA_1.50.0 googlesheets4_1.1.0
[10] workflowr_1.7.1 ggrepel_0.9.3 ggpubr_0.6.0
[13] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[16] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[19] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[22] tidyverse_2.0.0 SeuratDisk_0.0.0.9021 Seurat_5.0.1
[25] SeuratObject_5.0.1 sp_1.6-1
loaded via a namespace (and not attached):
[1] fs_1.6.2 matrixStats_1.0.0
[3] spatstat.sparse_3.0-1 bitops_1.0-7
[5] doParallel_1.0.17 httr_1.4.6
[7] tools_4.3.0 sctransform_0.4.1
[9] backports_1.4.1 utf8_1.2.3
[11] R6_2.5.1 HDF5Array_1.30.0
[13] lazyeval_0.2.2 uwot_0.1.14
[15] GetoptLong_1.0.5 rhdf5filters_1.14.1
[17] withr_2.5.0 gridExtra_2.3
[19] progressr_0.13.0 cli_3.6.1
[21] Biobase_2.62.0 Cairo_1.6-0
[23] spatstat.explore_3.2-1 fastDummies_1.7.3
[25] labeling_0.4.2 sass_0.4.6
[27] spatstat.data_3.0-1 ggridges_0.5.4
[29] pbapply_1.7-0 parallelly_1.36.0
[31] rstudioapi_0.14 RSQLite_2.3.1
[33] shape_1.4.6 generics_0.1.3
[35] ica_1.0-3 spatstat.random_3.1-5
[37] car_3.1-2 Matrix_1.6-5
[39] fansi_1.0.4 S4Vectors_0.40.2
[41] abind_1.4-5 lifecycle_1.0.3
[43] whisker_0.4.1 yaml_2.3.7
[45] carData_3.0-5 SummarizedExperiment_1.32.0
[47] rhdf5_2.46.1 SparseArray_1.2.3
[49] Rtsne_0.16 blob_1.2.4
[51] promises_1.2.0.1 crayon_1.5.2
[53] miniUI_0.1.1.1 lattice_0.21-8
[55] beachmat_2.18.0 cowplot_1.1.1
[57] annotate_1.80.0 KEGGREST_1.42.0
[59] magick_2.7.4 pillar_1.9.0
[61] knitr_1.43 GenomicRanges_1.54.1
[63] rjson_0.2.21 future.apply_1.11.0
[65] codetools_0.2-19 leiden_0.4.3
[67] glue_1.6.2 getPass_0.2-4
[69] data.table_1.14.8 vctrs_0.6.2
[71] png_0.1-8 spam_2.9-1
[73] cellranger_1.1.0 gtable_0.3.3
[75] cachem_1.0.8 xfun_0.39
[77] S4Arrays_1.2.0 mime_0.12
[79] survival_3.5-5 gargle_1.4.0
[81] SingleCellExperiment_1.24.0 iterators_1.0.14
[83] ellipsis_0.3.2 fitdistrplus_1.1-11
[85] ROCR_1.0-11 nlme_3.1-162
[87] bit64_4.0.5 RcppAnnoy_0.0.20
[89] GenomeInfoDb_1.38.5 rprojroot_2.0.3
[91] bslib_0.4.2 irlba_2.3.5.1
[93] KernSmooth_2.23-21 colorspace_2.1-0
[95] BiocGenerics_0.48.1 DBI_1.1.3
[97] tidyselect_1.2.0 processx_3.8.1
[99] curl_5.0.0 bit_4.0.5
[101] compiler_4.3.0 git2r_0.32.0
[103] graph_1.80.0 hdf5r_1.3.8
[105] DelayedArray_0.28.0 plotly_4.10.2
[107] scales_1.2.1 lmtest_0.9-40
[109] callr_3.7.3 digest_0.6.31
[111] goftest_1.2-3 presto_1.0.0
[113] spatstat.utils_3.0-3 rmarkdown_2.22
[115] XVector_0.42.0 htmltools_0.5.5
[117] pkgconfig_2.0.3 sparseMatrixStats_1.14.0
[119] MatrixGenerics_1.14.0 highr_0.10
[121] fastmap_1.1.1 GlobalOptions_0.1.2
[123] rlang_1.1.1 htmlwidgets_1.6.2
[125] shiny_1.7.4 DelayedMatrixStats_1.24.0
[127] farver_2.1.1 jquerylib_0.1.4
[129] zoo_1.8-12 jsonlite_1.8.5
[131] BiocParallel_1.36.0 BiocSingular_1.18.0
[133] RCurl_1.98-1.12 magrittr_2.0.3
[135] GenomeInfoDbData_1.2.11 dotCall64_1.0-2
[137] patchwork_1.1.2 Rhdf5lib_1.24.1
[139] munsell_0.5.0 Rcpp_1.0.10
[141] reticulate_1.29 stringi_1.7.12
[143] zlibbioc_1.48.0 MASS_7.3-60
[145] parallel_4.3.0 listenv_0.9.0
[147] deldir_1.0-9 Biostrings_2.70.1
[149] splines_4.3.0 tensor_1.5
[151] hms_1.1.3 ps_1.7.5
[153] igraph_1.4.3 spatstat.geom_3.2-1
[155] ggsignif_0.6.4 RcppHNSW_0.5.0
[157] reshape2_1.4.4 stats4_4.3.0
[159] ScaledMatrix_1.10.0 XML_3.99-0.14
[161] evaluate_0.21 foreach_1.5.2
[163] tzdb_0.4.0 httpuv_1.6.11
[165] RANN_2.6.1 polyclip_1.10-4
[167] clue_0.3-64 future_1.32.0
[169] scattermore_1.2 rsvd_1.0.5
[171] broom_1.0.4 xtable_1.8-4
[173] RSpectra_0.16-1 rstatix_0.7.2
[175] later_1.3.1 googledrive_2.1.0
[177] memoise_2.0.1 AnnotationDbi_1.64.1
[179] IRanges_2.36.0 cluster_2.1.4
[181] timechange_0.2.0 globals_0.16.2
[183] GSEABase_1.64.0